gregory etal aaea_12

SNAP and Diet Quality: A Treatment EffectsApproach

Christian A. Gregory* Shelly Ver Ploeg Margaret AndrewsAlisha Coleman-Jensen

Economic Research Service, USDA*contact author: [email protected].

Annual Meetings AAEASeattle, WA

August 13, 2012

The views expressed are those of the authors and shouldnot be attributed to ERS or USDA.

Background & Motivation

Background: Intent of Program

• SNAP authorizing legislation: “To alleviate such hunger andmalnutrition, a supplemental nutrition assistance program isherein authorized which will permit low-income households toobtain a more nutritious diet through normal channels of tradeby increasing purchasing power ...”

• food security and nutrition declared goals of SNAP

Gregory, Ver Ploeg, et al. SNAP and Diet Quality Aug. 13, 2012


Background: Public Perceptions

• “As I look at what this card is paying for in the orders beingscanned at the register, I see T-bone steaks, thick-cut sirloins,thick-cut pork chops (all expensive cuts of meat). I see crablegs, bags of shrimp, and box after box of pastries, cakes anddoughnuts from the bakery department, and bagged candy,chips and cookies from the snack aisles. Then come the sodas,energy drinks and Starbucks coffee drinks... The people usingthis card are eating better than most families that have twoincomes.” -Letter to Frederick News Post



Background: SNAP & Food Security

• recent research: SNAP ⇓ food insecurity

• Yen et al. (2008); DePolt et al. (2009); Shaefer and Gutierrez(2012); Nord and Golla (2009); Nord and Prell (2011);Ratcliffe et al. (2011)

• estimates suggest SNAP participation ⇓ food insecurity 33 -40 percent



Background: SNAP & Diet Quality

• recently–a good deal of concern

• many expensive chronic illnesses associated with low-incomepopulations

• public bears sizable fraction of cost

• policy suggestions:

– restrict foods eligible for SNAP (as in WIC)– Wholesome Wave Double Coupon– Healthy Incentives Pilot



Motivation

• large extant literature (detail below)

• some–improved intakes (Devaney and Moffitt, 1991; Wildeet al., 1999)

• some–poorer intakes (Butler and Raymond, 1996; Yen, 2010)

• difficult to identify treatment effects

selection on unobservables

• selection: adverse or beneficial?



Our Contribution

• use individual data (NHANES) matched to state-level dataidentify SNAP selection

• estimate treatment effects by isolating unobservables in SNAPand diet

• show that marginal effect of SNAP is positive and significantfor some HEI components; adverse selection accounts forworse diet outcomes



Preview of Results

• as measured by HEI total and component scores

– SNAP participants comparable diets– average treatment effect of SNAP (ATE): slightly lower HEIscores

– economically significant?– selection is adverse for many components– effect of SNAP on marginal participant is positive– in particular, SNAP gets participants to consume somewhole fruit and whole grains

• results corroborated by nutrient intakes

• robust to specification choice?

• suggest policy caution: tradeoff improving nutritional quality,changing selection into the program


Previous Research

Previous Research

• comprehensive review of literature (Fox et al., 2004)

• wrt intakes, few find significant impact ↑, ↓• highlight Gleason et al. (2000)–array of outcomes includingHEI–rule out large effects in either direction

• studies that find positive effects: Wilde et al. (1999);Kramer-LeBlanc et al. (1997); Basiotis et al. (1998)

• more recent studies: Cole and Fox (2008); Yen (2010)

• Waehrer and Deb (2012) used latent factor model/IV–SNAPparticipants ↑ caloric sweetened beverages ↓ fruits/vegetables


Data

Data: NHANES 2003-08

• individual: NHANES 2003-04, 2005-06, 2007-08

• dependent variable: Healthy Eating Index Score (HEI) (day 1), total andcomponent

– total = sum of 12 elements– total fruit, whole fruit, total veg, dark green and orange veg, total

grains, whole grains, milk, meat and beans, oils, sat fat, sodium,SoFAAS

– for food groups and oils: zero intake = score of zero; meet/exceeddietary recommendation = perfect score; linear interpolation b/w


Data

Data: NHANES 2003-08 (cont)

• dependent variable: Healthy Eating Index Score (HEI) (day 1), total andcomponent (continued)

• how to score “moderation” components? (i.e. things you should eat lessof)

– 85th pctile of consumption = score of zero; meet Dietary Guidelinesrecommendation = score of 8; meet somewhat higher standard, belowdietary rec = score of 10; linear interpellation b/w amounts at 0 and 8,8 and 10.

– example: sat fat. – fraction of total energy (2001-2002 NHANES data)

• 85th pctile: 15 % : score of 0• DG: less than 10 %: score of 8• below 7% : score of 10

– weights: milk, meat/beans, oils, sat fat, sodium = 10; total fruit, wholefruit, total veg, dark green and orange veg, total grains, whole grains=5 ; SoFAAS = 20


Data

Data: NHANES 2003-08

• independent variable of interest: HH SNAP participation

– 2003, 2005 waves: 2 questions HH SNAP participation: number ofpersons authorized to receive SNAP, whether HH receive SNAP 12 mos.

– 2007 wave: HH receive SNAP 12 mos– we use whether HH receive SNAP 12 mos 2003, 2005, 2007– robustness check: sample person currently receiving SNAP

• other rhs variables: race/ethnicity, income, education, SR weight 1 yearago, age, marital status, employment status, vigorous ex./week, nutritioned per poor person, hh size, state fixed-effects

• 200% FPL


Data

Data: SNAP Policy Database

• in model (following) we need exogenous variables to identifyparticipation in SNAP

– state-month level variation in three policies:– expanded categorical eligibility–relaxed asset and/orincome requirements

– biometric info needed to enroll–usually a fingerprint– certification period–median certification period forhouseholds with earnings calculated from the QC data

• valid: the policies affect SNAP participation but not dietquality/HEI


Methods

Selection Model

• one might begin with

HEIi = Xiβ + SNAPiδOLS + ϵi (1)

• problem: SNAP is endogenous to HEI• another way to proceed

HEIi = Xiβ + SNAPiδZ + ϵi (2)

SNAP∗i = Ziγ + Xiθ + υi (3)

• Z exogenous variables for SNAP• SNAP∗ latent index of SNAP participation• X other variables correlated w/ SNAP, HEI• ϵ and υ bivariate normal w/covariance matrix

V =

[σ2 ρσρσ 1

]Gregory, Ver Ploeg, et al. SNAP and Diet Quality Aug. 13, 2012

Methods

Identification & Marginal Effects

• model is theoretically identified by functional form imposed bydistribution of ϵ and υ.

• we use exogenous policy variables to identify SNAPparticipation

• ATE of SNAP :

µi = δZ + ρσ

[ϕ(Ziγ + Xiθ)

Φ(Ziγ + Xiθ) ∗ [1− Φ(Ziγ + Xiθ)]

](4)

this is what δOLS will estimate


Methods

Identification & Marginal Effects

• without selection: µi = δOLS ; with selection δZ + difference inexpected value of errors conditional on participation (SeeGreene, 2011)

• unconditional on selection, δZ measures marginal affects ofSNAP on participants

• standard errors (of total effects) (ν) by delta method: letα = [γ, θ]

νµ =

√∂µ

∂αM

∂µ

∂α

′, (5)

where M is the covariance matrix of the selection equation


Results

Descriptive

51.8

47.8

4950

5152

53H

EI S

core

No SNAP SNAP Participants

Data: NHANES, 2003−08

SNAP Participation Status

HEI Score and SNAP Participation

Figure : Differences in HEI over SNAP Participation


Results

Descriptive

2094

2124.3

2044

2074

2104

2134

To

tal E

ner

gy

Inta

ke

No SNAP SNAP Participants

Data: NHANES, 2003−08

SNAP Participation Status

Total Food Energy and SNAP Participation

Figure : Differences in Energy over SNAP Participation


Results

Descriptive

Table : Means of HEI Components by SNAP Participation

HEI Component No SNAP SNAP Difference

TotalFruit 2.11 1.73 -0.38***(0.07) (0.07) (0.12)

WholeFruit 1.93 1.39 -0.54***(0.06) (0.06) (0.10)

TotalVeg 3.00 2.63 -0.37***(0.04) (0.07) (0.08)

DkGOrVeg 1.17 0.83 -0.34***(0.05) (0.05) (0.08)

TotGrain 4.27 4.07 -0.20***(0.03) (0.04) (0.06)

WholeGrain 0.93 0.66 -0.27***(0.04) (0.03) (0.05)

N 5,105


Results

Descriptive

Table : Means of HEI Components by SNAP Participation, cont’d

HEI Component No SNAP SNAP Difference

Milk 4.77 4.39 -0.38**(0.09) (0.11) (0.15)

Sodium 4.12 4.52 0.40***(0.07) (0.09) (0.11)

SoFAAS 9.47 7.96 -1.51***(0.20) (0.25) (0.41)

N 5,105


Results

ATE of SNAP

Table : ATE of SNAP on HEI/Components: 200% FPL

HEI TotalFruit WholeFruit TotalVeg DkGOrVeg

µ -1.241*** -0.144*** -0.520*** -0.069*** -0.103***νµ (0.049) (0.016) (0.082) (0.009) (0.005)

TotGrain WholeGrain Milk MeatBeans Oils

µ -0.094*** -0.307*** 0.004 -0.340*** 0.039**νµ (0.005) (0.078) (0.004) (0.000) (0.017)

SatFat Sodium SoFAAS

µ 0.0290*** 0.376*** -0.388***νµ (0.009) (0.001) (0.039)N 5,105


Results

Correlation, IV Strength

Table : Selection Paramter: ρ


ρ 0.082 -0.107 -0.648*** 0.071 0.040νρ (0.169) (0.223) (0.203) (0.129) (0.301)


ρ -0.059 -1.032*** -0.017 -0.000 0.066νρ (0.048) (0.069) (0.096) (0.084) (0.106)


ρ -0.035 0.003 0.082νρ (0.127) (0.117) (0.169)

• All F-tests of instruments > 15.


Results

Marginal Effects of SNAP

Table : Marginal Effects of SNAP=δZ


δ -1.429 0.270 1.981*** -0.301 -0.236νδ (1.916) (0.757) (0.624) (0.382) (0.870)


δ 0.041 1.940*** 0.116 -0.338 -0.425νδ (0.133) (0.095) (0.598) (0.392) (0.697)


δ 0.273 0.357 -1.429νδ (0.908) (0.670) (1.916)

N 5,105


Results

Questions

• δs seem too large to be believed

• δwf = 1.98, x̄ = 1.39

• δwg = 1.94, x̄ = .66


Results

Distribution of Components

0.1

.2.3

.4.5

Den

sity

0 1 2 3 4 5Score

Data: NHANES 2003−08, 200% FPL Kernel Density WholeFruit Component Score

0.5

11.

5D

ensi

ty

0 1 2 3 4 5Score

Data: NHANES 2003−08, 200% FPL Kernel Density WholeGrain Component Score

Figure : Distribution of Whole Fruit, Whole Grain Components

• modewf = 0, modewg = 0


Results

Distributional Concerns

• need to address the violation of distributional assumptions

• GMM, 2SLS, larger std errs, size of δZ still a concern

• finite mixture model (latent class model) – probabilities asfunction of SNAP participation (in process)


Results

Solution: Bivariate Probit

Table : Bivariate Probit: Effect of SNAP on Score >0

Whole Fruit Whole Grain

Parameter Marginal Effect Parameter Marginal Effect

SNAP 0.672** 0.409 .699*** 0.409(0.29) (0.22)

N 5,105

• effect on SNAP is to increase by 40 percentage points pointsprob of eating any whole fruit or whole grains

• too large? less than 30% of sample eat any whole fruit orwhole grain


Results

Total Effects: Current Recipients

Table : Total Effects of SNAP (Current) on HEI/Component Scores


µ -2.371*** -0.301*** -0.570*** -0.059*** -0.019νµ (0.601) (0.093) (0.137) (0.013) (0.017)


µ -0.089*** -0.357*** 0.0570*** -0.352*** -0.076***νµ (0.007) (0.102) (0.004) (0.019) (0.005)


µ 0.179*** 0.337*** -0.712***νµ (0.007) (0.028) (0.139)

N 5,105


Results

Marginal Effects: Current Recipients

Table : Marginal Effect of SNAP (Current) = δZ


δ 5.245 0.897 2.981*** -0.690 -0.674***νdelta (11.316) (1.102) (0.200) (0.514) (0.180)


δ 0.053 1.984*** 0.554 -0.264 -0.277νdelta (0.158) (0.073) (0.614) (0.302) (0.934)


δ 0.108 -0.313 0.203νdelta (0.951) (0.542) (2.326)

N 5,105

• similar marginal effects of SNAP on score > 0.


Results

Robustness: Nutrient Intake

Table : Total Effects of SNAP on Nutrient Intake

Energy (Kcal) Protein Total Fat Sat Fat Carbs

µ -19.78*** -0.047*** -1.810*** -0.221*** 0.711***νµ (1.87) (0.02) (0.31) (0.05) (0.129)

Vitamin C Niacin Folate Sodium Frac FAFH

µ 8.220*** 0.166*** -0.063*** -0.208*** -0.029***νµ (0.08) (0.02) (0.01) (0.00) (0.00)

N 5,105


Discussion

Discussion

• Results

– SNAP participants slightly lower HEI scores thancomparable non-participants

– ATE statistically significant, though not economically so– ATE for current recipients somewhat larger–same directions– corroborated by nutrient intake results– however: adverse selection into SNAP– SNAP has positive effect on whole fruit and whole grainconsumption of SNAP participants ⇑ in P(Score) > 0.

– but participants in general have slightly less healthy dietscompared to similar non-participants


Discussion

Discussion

• Further Questions

– controlled for endogeneity fully?– distribution of error terms–alternative distributions– how might SNAP improve DQ w/o adversely affectingselection/effectiveness?

– subsidies instead of restrictions? (Wholesome Wave, HealthyIncentives)


Discussion

Further Discussion?

Thank You


gregory etal aaea_12

Health & Medicine

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nutritious diet

snapand diet

diet qualityaug

nutritional quality

data data

hei total

nhanes data